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    <title language="eng">Imaging with Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images</title>
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    <abstract language="eng">Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.</abstract>
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    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>1384</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>99</pageFirst>
    <pageLast>122</pageLast>
    <pageNumber/>
    <edition/>
    <issue>82</issue>
    <volume>2022</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data harmonisation for information fusion in digital healthcare: A&#13;
state-of-the-art systematic review, meta-analysis and future&#13;
research directions</title>
    <abstract language="eng">Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research</abstract>
    <parentTitle language="eng">Information Fusion</parentTitle>
    <identifier type="issn">1566-2535</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13845</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.inffus.2022.01.001</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Yang</first_name>
      <last_name>Nan</last_name>
    </author>
    <author>
      <first_name>Javier</first_name>
      <last_name>Del Ser</last_name>
    </author>
    <author>
      <first_name>Simon</first_name>
      <last_name>Walsh</last_name>
    </author>
    <author>
      <first_name>Carola</first_name>
      <last_name>Schönlieb</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Roberts</last_name>
    </author>
    <author>
      <first_name>Ian</first_name>
      <last_name>Selby</last_name>
    </author>
    <author>
      <first_name>Kit</first_name>
      <last_name>Howard</last_name>
    </author>
    <author>
      <first_name>John</first_name>
      <last_name>Owen</last_name>
    </author>
    <author>
      <first_name>Jon</first_name>
      <last_name>Neville</last_name>
    </author>
    <author>
      <first_name>Julien</first_name>
      <last_name>Guiot</last_name>
    </author>
    <author>
      <first_name>Benoit</first_name>
      <last_name>Ernst</last_name>
    </author>
    <author>
      <first_name>Ana</first_name>
      <last_name>Pastor</last_name>
    </author>
    <author>
      <first_name>Angel</first_name>
      <last_name>Alberich-Bayarri</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Sean</first_name>
      <last_name>Walsh</last_name>
    </author>
    <author>
      <first_name>Wim</first_name>
      <last_name>Vos</last_name>
    </author>
    <author>
      <first_name>Nina</first_name>
      <last_name>Flerin</last_name>
    </author>
    <author>
      <first_name>Jean-Paul</first_name>
      <last_name>Charbonnier</last_name>
    </author>
    <author>
      <first_name>Eva van</first_name>
      <last_name>Rikxoort</last_name>
    </author>
    <author>
      <first_name>Avishek</first_name>
      <last_name>Chatterjee</last_name>
    </author>
    <author>
      <first_name>Henry</first_name>
      <last_name>Woodruff</last_name>
    </author>
    <author>
      <first_name>Philippe</first_name>
      <last_name>Lambin</last_name>
    </author>
    <author>
      <first_name>Leonor</first_name>
      <last_name>Cerdá-Alberich</last_name>
    </author>
    <author>
      <first_name>Luis</first_name>
      <last_name>Martí-Bonmatí</last_name>
    </author>
    <author>
      <first_name>Francisco</first_name>
      <last_name>Herrera</last_name>
    </author>
    <author>
      <first_name>Guang</first_name>
      <last_name>Yang</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Information fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data harmonisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data standardisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>domain adaptation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reproducibility</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1384/1-s2.0-S156625352200015X-main.pdf</file>
  </doc>
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    <id>1386</id>
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    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>77</issue>
    <volume>2022</volume>
    <articleNumber>102387</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning residual motion correction for fast and robust 3D multiparametric MRI</title>
    <abstract language="eng">Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-13861</identifier>
    <identifier type="issn">1361-8423</identifier>
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    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Matteo</first_name>
      <last_name>Cencini</last_name>
    </author>
    <author>
      <first_name>Jan W.</first_name>
      <last_name>Kurzawski</last_name>
    </author>
    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
    </author>
    <author>
      <first_name>Hongwei</first_name>
      <last_name>Li</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiparametric MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D Motion correction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Residual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiscale CNN</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1386/1-s2.0-S1361841522000391-main.pdf</file>
  </doc>
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    <id>2814</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber>4</pageNumber>
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    <publisherPlace>Piscataway</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-09-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers</title>
    <parentTitle language="eng">Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)</parentTitle>
    <identifier type="isbn">978-1-6654-2923-8</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ISBI52829.2022.9761603</enrichment>
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    <enrichment key="THI_conferenceName">2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata (India) &amp; online, 28.-31.03.2022</enrichment>
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    <author>
      <first_name>Ketan</first_name>
      <last_name>Fatania</last_name>
    </author>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Peter</first_name>
      <last_name>Hall</last_name>
    </author>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantitative MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Compressed Sensing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse Problems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Iterative Image Reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plug-and-Play</value>
    </subject>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>4623</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
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    <publisherPlace>Piscataway</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI</title>
    <parentTitle language="eng">2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)</parentTitle>
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    <author>
      <first_name>Ketan</first_name>
      <last_name>Fatania</last_name>
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    <author>
      <first_name>Kwai Y.</first_name>
      <last_name>Chau</last_name>
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    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
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      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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      <first_name>Mohammad</first_name>
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    <publishedYear>2024</publishedYear>
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    <pageFirst>128</pageFirst>
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    <completedDate>2024-11-05</completedDate>
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    <title language="eng">StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations</title>
    <parentTitle language="eng">Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II</parentTitle>
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